Data from: Combining ground‐penetrating radar with terrestrial LiDAR scanning to estimate the spatial distribution of liquid water content in seasonal snowpacks
Webb, R. W. et al. (2018), Data from: Combining ground‐penetrating radar with terrestrial LiDAR scanning to estimate the spatial distribution of liquid water content in seasonal snowpacks, Dryad, Dataset, https://doi.org/10.5061/dryad.hn626p7
Many communities and ecosystems around the world rely on mountain snowpacks to provide valuable water resources. An important consideration for water resources planning is runoff timing, which can be strongly influenced by the physical process of water storage within and release from seasonal snowpacks. The aim of this study is to present a novel method that combines light detection and ranging with ground‐penetrating radar to nondestructively estimate the spatial distribution of bulk liquid water content in a seasonal snowpack during spring snowmelt. We develop these methods in a manner to be applicable within a short time window, making it possible to spatially observe rapid changes that occur to this property at subdaily timescales. We applied these methods at two experimental plots in Colorado, showing the high variability of liquid water content in snow. Volumetric liquid water contents ranged from near zero to 19%vol within the scale of meters. We also show rapid changes in bulk liquid water content of up to 5%vol that occur over subdaily timescales. The presented methods have an average uncertainty in bulk liquid water content of 1.5%vol, making them applicable for future studies to estimate the complex spatio‐temporal dynamics of liquid water in snow.
National Science Foundation, Award: 1624853